An autoregressive Hamiltonian neural network coupled with an unscented Kalman filter improves long-term trajectory prediction and uncertainty quantification for unknown Hamiltonian systems.
Structural schemes for hamiltonian systems
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abstract
We present an adaptation of the so-called structural method \cite{CMM23} for Hamiltonian systems, and redesign the method for this specific context, which involves two coupled differential systems. Structural schemes decompose the problem into two sets of equations: the physical equations, which describe the local dynamics of the system, and the structural equations, which only involve the discretization on a very compact stencil. They have desirable properties, such as unconditional stability or high-order accuracy. We first give a general description of the scheme for the scalar case (which corresponds to e.g. spring-mass interactions or pendulum motion), before extending the technique to the vector case (treating e.g. the $n$-body system). The scheme is also written in the case of a non-separable system (e.g. a charged particle in an electromagnetic field). We give numerical evidence of the method's efficiency, its capacity to preserve invariant quantities such as the total energy, and draw comparisons with the traditional symplectic methods.
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Learning Hamiltonian Dynamics with Bayesian Data Assimilation
An autoregressive Hamiltonian neural network coupled with an unscented Kalman filter improves long-term trajectory prediction and uncertainty quantification for unknown Hamiltonian systems.